class_docstr: Computes the standard deviation for each metric from samples.
class_docstr_tok: ["Computes","the","standard","deviation","for","each","metric","from","samples","."]
code: def merge_accumulators(
      self, accumulators: Iterable["SampleCombineFn.SampleAccumulator"]
  ) -> "SampleCombineFn.SampleAccumulator":
      # treat as iterator to enforce streaming processing
      accumulators = iter(accumulators)
      result = next(accumulators)
      for accumulator in accumulators:
          if accumulator.point_estimates is not None:
              result.point_estimates = accumulator.point_estimates
          result.num_samples += accumulator.num_samples
          for (
              metric_key,
              sample_values,
          ) in accumulator.metric_samples.items():
              result.metric_samples[metric_key].extend(sample_values)
      return result
  
file_path: model-analysis/tensorflow_model_analysis/evaluators/confidence_intervals_util.py
imports: import collections
  import numbers
  from typing import Iterable, NamedTuple, Optional, Sequence, Set, Tuple
  import apache_beam
  import numpy
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.post_export_metrics import metric_keys
label_desc: Use image node_id 4 for calling the SampleCombineFn obj's underlying member method code with example usage: obj.merge_accumulators(accumulators) and returns: result